It is found that, under certain conditions, the estimation errors produced by the Standard Kalman-filter algorithm increase rapidly, and become unbounded, even though the predicted error covariance continues to decrease in accordance with the stability properties of the Kalman filter. A very simple modification, which freezes the filter gain when divergence is suspected, is suggested. The modified algorithm would keep these errors within bound without causing an appreciable increase in the computation burden.